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A Landscape Approach for Detecting and Assessing Changes in an Area Prone to Desertification in Sardinia (Italy)

A Landscape Approach for Detecting and Assessing Changes in an Area Prone to Desertification in... Hindawi Publishing Corporation International Journal of Navigation and Observation Volume 2008, Article ID 549630, 5 pages doi:10.1155/2008/549630 Research Article A Landscape Approach for Detecting and Assessing Changes in an Area Prone to Desertification in Sardinia (Italy) 1, 2 1 Francesca Giordano and Alberto Marini TELEGIS Laboratory, Earth Sciences Department, University of Cagliari, Via Trentino 51, 09127 Cagliari, Italy Department State of the Environment and Environmental Metrology, Italian Agency for Environmental Protection and Technical Services (APAT), Via V. Brancati 48, 00144 Rome, Italy Correspondence should be addressed to Francesca Giordano, fragisi@tin.it Received 25 September 2007; Revised 6 December 2007; Accepted 17 March 2008 Recommended by Marco Gianinetto Land degradation and desertification processes represent a serious problem in many areas of Sardinia (Italy), as in the Nurra region where urbanization, overgrazing, and fires have induced environmental degradation and rapid land-use change. In this study, using satellite remote sensing and geographical information system, landcover and landscape change dynamics were investigated. Comparing two Landsat-5 Thematic Mapper, it was possible to assess landcover transformations, and with the FRAGSTATS software it was possible to quantify the changes of landscape characteristics in the Nurra region over a 10-years period. The images were classified into seven landcover types, and a stepwise indicator approach was adopted. The results show a decrease in cropland and an increase of forestland and urban areas. The overall change was estimated to be about 2.5% of the total study area, with two most frequent landcover conversion types: cropland to urban areas and cropland to forestland. Copyright © 2008 F. Giordano and A. Marini. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. INTRODUCTION Due to its particular geographical position and its extreme climatic variations such as droughts and floods, In the last two centuries, the impact of human activities on Sardinia can be considered a representative area of the the land has grown enormously, altering entire landscapes typical environmental problems of the Mediterranean basin. Desertification processes, in particular, represent a serious with important ecological consequences like biodiversity loss, deforestation, soil erosion, and desertification. threat to many areas on the island where the impact of human activities on the environment is overlapped to the According to article 1 of the United Nations Convention climatic stress. In the last decades, the urbanization along to Combat Desertification (UNCCD) desertification is “land the coastal areas has strongly increased due to new tourist degradation in arid, semi-arid and dry sub-humid areas settlements and urban infrastructures. Not only urbanization resulting from various factors, including climatic variations but also the loss of fertile soils, massive water exploitation, and human activities”[1]. Instead, land degradation is a overgrazing, and fires are the other important reasons for reduction in the biological and economic productivity of the environmental problems in the Nurra region, the north- terrestrial ecosystems, including soil, vegetation, and biota. western part of the Island. Many scientific projects such Land degradation also disturbs the ecological, biogeochemi- as DesertNet [2], RIADE [3], and DesertWatch [4]have cal, and hydrological processes. previously investigated this area with results demonstrating Before the presence of the man, natural processes shaped the high sensitivity to desertification of the area. the landscape, but after the man came to Earth, Earth’s face has changed. In the past two centuries, the impact of human The objective of the present study was to detect, char- agricultural, industrial, and extractive activities, combined acterize, and quantify the changes in terms of landcover with natural and human actions, induced climatic variations and landscape in an area sensitive to land degradation and which led to land degradation on an unprecedented scale. desertification in the Nurra region. In the present study, 2 International Journal of Navigation and Observation was used as a reference image to geo-reference the other image within acceptable limits (rms = 0.71 pixels for the 1990 scene), and the nearest neighbour algorithm was used as the resampling method [10]. Atmospheric correction is not strictly necessary when postclassification change detection is the method selected for the analysis of changes. In this case, each image is individually classified and then the resulting maps are compared [11]. In this approach, supervised classification was used in order to perform a landcover map for each image. In this type of classification, the user selects the spectral signatures defined from recognized locations in the image or “training sample.” The computer system then identifies the pixels with similar characteristics and assigns them to a class based on specific criteria. About 50 sample data were defined with help of ortophotos (scale 1 : 10.000) and available maps. These tools helped classify a seven-class legend: urban areas, forestland, shrubland, cropland, barren land, wetlands, and water bodies [12]. Supervised classification was then performed using the maximum likelihood method, in which a pixel with the maximum likelihood is classified Figure 1: Nurra and town of Sassari in Sardinia (Italy). to its corresponding class. The accuracy assessment showed that the overall accuracy of the landcover maps approached 88% for the 1990 image and 92% for the 2000 image. The remote sensing techniques were applied in order to analyze existence of mixed pixels (pixels having more than one class landcover change from 1990 to 2000 by Landsat Thematic in their footprint), in particular among vegetation classes, Mapper TM images interpretation and to assess landscape would require an analysis at a higher geometric resolution structure change by using FRAGSTATS, a spatial pattern or a comparison with multitemporal data to exploit the analysis program software package [5]. phenological curves. This analysis would be useful in order to find if the classification accuracy could further improve. After performing the landcover maps, the next step was to 2. STUDY AREA overlay them and create a landcover change map (1990- The study area (ca 40 43 N, 8 34 E) is located in the 2000). north-western part of Sardinia (Italy) (see Figure 1)and In order to perform the landcover and landscape dynam- covers four municipalities: Sassari, Alghero, Stintino, and ics analysis of the study area, a stepwise indicator approach Porto Torres. This area of about 88400 ha is characterized by was used to measure the various aspects of landcover and a high geological and morphological complexity. The climate landscape and their change [13–15]. is typically Mediterranean dry-subhumid with abundant Landcover maps represent the source of landcover indica- amount of rainfall during the autumn-winter period and a tors (1990 and 2000) and landscape metrics (1990 and 2000). scarce amount of rainfall with very high temperature during In particular, landcover indicators aimed at the evalua- the summer period. Mean annual precipitation values vary tion of the extent of each landcover class in 1990 and in 2000 between 490 mm and 870 mm. The area belongs to the and measured the direction of change (increase/decrease) of phytoclimatic area of Lauretum. each class during the study period. As demonstrated from recent studies [6], the area is Landscape metrics measure the spatial structure of strongly sensitive to land degradation and desertification landcover metrics in terms of composition (number, pro- with more than half of the territory belonging to critical portional frequency, and diversity of landscape elements environmentally sensitive areas (ESAs) or “areas already within the landscape) and configuration (spatial position highly degraded through past misuse, presenting a threat to and distribution of the elements within the landscape). In the environment of the surrounding areas” [7]. order to calculate landscape metrics, landcover maps were converted into Grid format using ERDAS Imagine [8, 9]and introduced into the FRAGSTATS software [5]. 3. METHODS In particular landscape metrics at class level, measuring Two Landsat satellite Thematic Mapper TM images were the aggregate properties of the patches belonging to a single selected over the study area. The first image was a Landsat- class or patch type, and landscape metrics at landscape level, measuring the aggregate properties of the entire patch 5 Thematic Mapper of May 12, 1990, with seven spectral bands and a spatial resolution of 30 m. The second one mosaic, were performed. was a Landsat-5 Thematic Mapper of June 27, 2000. For Landcover change map (1990-2000) represents the source of landcover change indicators, which aim at the the analysis of Landsat satellite images, ERDAS Imagine 8.5 software was used [8, 9]. Landsat 5 TM of June 27, 2000 location of the areas of change and at the evaluation of the F. Giordano and A. Marini 3 Table 1: Landcover areas per classes in the study area, 1990 and 2000. Area in 1990 Area in 2000 Class Class Direction of change ha % ha % Cropland 67.209,5 76,0 Cropland 65.956,9 74,6 decrease Forestland 8.246,4 9,3 Forestland 9.088,6 10,3 increase Shrubland 7.528,8 8,5 Shrubland 7.487,8 8,5 — Urban areas 4.033,2 4,6 Urban areas 4.455,7 5,0 increase Barren 1.064,2 1,2 Barren 1.118,6 1,3 slight increase Wetlands 230,3 0,3 Wetlands 251,9 0,3 slight increase Water bodies 75,9 0,1 Water bodies 28,8 0,0 decrease Total 88.388,2 100,0 Total 88.388,2 100,0 Table 2: Landscape metrics at class level, 1990 and 2000. CA PLAND NP PD AREA MN SHAPE AM PROX MN IJI Class (ha) (%) (#) (#/100 ha) (ha) (ha) (m) (%) Cropland 67210 29,2 1830 0,8 36,8 21,8 56710 63,7 Forestland 8246 3,6 5140 2,2 1,6 5,8 71,2 54,9 Urban areas 4033 1,8 1775 0,8 2,3 5,9 112,2 33,4 Cropland 65957 28,7 1660 0,7 39,7 17,6 66550 69,8 Forestland 9088 3,9 4190 1,8 2,2 7,3 212,2 61,0 Urban areas 4455 1,9 689 0,3 6,5 5,9 301,3 48,0 extent of changes for the total study area and for each increase of riparian vegetation, with a consequent silting up landcover class. These indicators measure also the landcover of the shallow waters, in particular along the coast of the transition direction and the gains and losses for each class Baratz lake. during the study period. The entire set of landcover and landscape indicators 4.2. Landscape indicators at class level has been setup in order to answer the following questions. Where are the landcover changes (location)? Which is the Landcover maps were the source for the landscape metrics magnitude of landcover change (extension)? Which is the computation at class level and at landscape level by means of direction of landcover change (direction)? Which are the FRAGSTATS software [5]. spatial characteristics of landscape change (structure)? Table 2 shows the most relevant landscape metrics performed at class level for the entire study area and for 4. RESULTS the most changing classes (cropland, forestland, and urban areas) [16–18]. For the analysis and the comprehension of 4.1. Landcover indicators landscape metrics at class level, it is advisable not to analyze Once the classification of each image was performed, and the just a landscape metric but rather a set of metrics to better landcover maps for each period of study were obtained, the understand and describe the dynamics of ecosystems and first step was to quantify each landcover class extension over landscape structures. the total area (see Table 1). Cropland in the study area represents the major class of Cropland is largely the dominant landcover type in the the landscape. The study shows a decrease of cropland in study area in both cases, followed by forestland, shrubland, terms of the percentage of landscape (PLAND, proportional and urban areas. In particular during the period from 1990 abundance of each patch type in the landscape), the number to 2000, cropland decreased from 76% to 74.6%, while of patches (NP, extent of subdivision or fragmentation of the forestland and urban areas increased in area (from 9.3% patch type), and the patch density (PD, number of patches to 10.3% and from 4.6% to 5%, resp.) and shrubland on a per unit area) during the period from 1990 to 2000. maintained the same dimensions. Furthermore, a sharp Instead, the area weighted mean shape index (SHAPE AM, decrease of water bodies occurred. mean patch shape complexity weighted by patch area) shows The increase of urban areas, due to the continuous the highest values both in 1990 and 2000 for cropland urbanization process in the Nurra region during the last in the study area and shows a decline during the study years, is not a direct cause of the decrease in the area of water period from 21.8 to 17.6 indicating a reduction in the shape bodies. The variation in water bodies is probably due to the complexity. Shape complexity relates to the geometry of 4 International Journal of Navigation and Observation Table 3: Landscape metrics at landscape level, 1990 and 2000. NP PD LPI AREA MN SHAPE AM PROX MN IJI PR SHDI SHEI Year (#) (#/100 ha) (%) (ha) (ha) (m) (%) — — — 1990 11960 5,2 28,4 7,4 17,9 8750 55,9 7 0,85 0,44 2000 9840 4,3 27,5 9,0 14,6 11390 61,1 7 0,89 0,46 patches whether they tend to be simple and compact or The spatial context of landscape patches also had some irregular and convoluted. changes. For instance, the mean proximity index became In regards to the forestland during the period 1990– greater (from 8750 to 11390) displaying that the spatial dis- 2000, the number of patches (NP) decreased from 5140 tribution of patches became more continuous. In addition, to 4190, the mean patch area (AREA MN, average size of the increase of interspersion and juxtaposition index (from patches) increased from 1.6 to 2.2, and the mean proximity 55.9 to 61.1), together with the increase of mean proximity index (PROX MN, average proximity index for all patches index, indicated a more uniform landscape configuration. in a class) increased from 71.2 to 212.2. This indicates that patches of the same type increasingly occupy the neighbor- 4.4. Landcover change indicators hood between forested patches, which is defined by 300 m, and those patches have become closer and more contiguous From the overlaying of the two landcover maps, a landcover in distribution. In this sense, the forested landscape has change area (1990–2000) was estimated to be about 2.5% of become less fragmented [19]. the total study area. Now, we do not have enough ground The analysis of urban areas indicated that the number of information to be able to rule out errors in the classification. patches (NP) significantly decreased from 1775 to 689, while Considering that there is 8%–12% error in classification the mean patch area (AREA MN) increased from 2.3 to 6.5 accuracy, we expect to improve the analysis through further and the class area (CA, total area of all patches per class) ground points all over the changed areas in order to increase increased from 4033 to 4455. This combination of results the significance of the result. shows that new urban buildings generally occur in the voids Two landcover conversions were the most frequent in the of the core or adjacent to existent urban patches indicating study area: conversion from cropland to urban areas (45.7%) that urban areas grow in a concentrated way [20]. Finally, and conversion from cropland to forestland (44.5%). In the increase of the mean proximity index (from 112 to 301) Sassari, Alghero, and Stintino municipalities, the overlay and of the interspersion and juxtaposition index (from 33,4 of the landcover change map within the administrative to 48,0) of urban class shows a more uniform landscape limits showed that the percentage of landcover was mainly configuration. The first index indicates an increase in the composed by cropland followed by forestland, shrubland, proximity of urban patches, which has become closer and and urban areas. The only difference found was in Porto more contiguous in distribution. As for the second index, it Torres, where the landscape was dominated by cropland measures an increase of the interspersion (or intermixing of followed by urban areas, which represented the second patch types) of urban patches (more equally adjacent to each landscape class in terms of spatial extension. other). The major landcover changes occurred in the Sassari municipality (3%), followed by the Alghero municipality 4.3. Landscape indicators at landscape level (1.7%) and finally by the Porto Torres and Stintino munic- ipalities (1.2%). In particular, the conversion from cropland Table 3 shows the most relevant landscape metrics per- to urban areas represented 52% of the changes in the Sassari formed at landscape level for the study area. area, 26.3% of changes in the Alghero area, and 61.7% of As seen in Table 3, during the decade 1990–2000 the the changes in the Porto Torres area and, in conclusion, patch number (NP) in the study area decreased (from 11960 45.6% of the changes in the Stintino area were related to the to 9840), and the mean patch area (AREA MN) increased conversion from cropland to forestland. (from 7.4 to 9.0), showing a trend towards an increasingly large-grained landscape. Shannon’s diversity (SHDI) and evenness (SHEI) indices 5. CONCLUSIONS both became greater, showing that the landscape heterogene- ity and evenness slightly increased. Shannon’s diversity index The analysis carried out showed that the landscape of the is, in fact, a popular measure of diversity in community study area is dominated by cropland, followed by forestland ecology, applied here to landscapes as a measure of the and shrubland, and urban areas. During the study period equitability of the number of patch types and of the (1990–2000) cropland decreased, while forestland and urban proportional distribution of area among patch types. Shan- areas increased. The dynamics of change revealed that non evenness index is another popular diversity measure forested landscape has become less fragmented during the borrowed from community ecology, indicating the evenness decade thus avoiding the breakup of natural areas into of the distribution of area among the different patch types smaller and more isolated units. As for the urban areas, [5]. they showed growth in a concentrated way, as new urban F. Giordano and A. Marini 5 buildings generally occurred in the voids of the core or evaluating change in a semi-arid environment,” Environmental Monitoring and Assessment, vol. 64, no. 1, pp. 179–195, 2000. adjacent to existent urban patches. [13] F. Herzog and A. Lausch, “Supplementing land-use statistics The overall landcover change in the area was estimated to with landscape metrics: some methodological considerations,” be about 2.5% of the total study area, with two most frequent Environmental Monitoring and Assessment, vol. 72, no. 1, pp. landcover conversion types: 37–50, 2001. (i) conversion from cropland to urban areas (45.7%), [14] A. Lausch, “Assessment of landscape pattern and landscape functions by application of GIS and remote sensing,” in (ii) conversion from cropland to forestland (44.5%). Proceedings of the 3rd International Conference on Ecosystems The major landcover changes occurred in the Sassari munic- and Sustainable Development (ECOSUD ’01),Y.Villacampa, ipality (3%), followed by the Alghero municipality (1.7%). C. A. Brebbia, and J. L. Uso, Eds., vol. 10, pp. 367–376, The present work describes the analysis performed in WITPRESS, Alicante, Spain, June 2001. [15] A. Lausch and H. Thulke, “The analysis of spatio-temporal terms of landcover and landscape change by means of dynamics of landscape structures,” in Landscape Balance and remote sensing and GIS techniques in an area prone to land Landscape Assessment, R. Kronert, U. Steinhardt, and M. Volk, degradation and desertification in Sardinia (Italy) during the Eds., pp. 113–136, Springer, Berlin, Germany, 2001. period 1990–2000. For this purpose, a set of landcover and [16] S. Weiers, M. Bock, M. Wissen, and G. Rossner, “Mapping and landscape indicators was setup, and a quantitative charac- indicator approaches for the assessment of habitats at different terization of changes was performed. The combination of scales using remote sensing and GIS methods,” Landscape and Landsat data, GIS, and FRAGSTATS software was generally Urban Planning, vol. 67, no. 1–4, pp. 43–65, 2004. helpful in providing techniques to monitor landcover and [17] Y.-C. Weng, “Spatiotemporal changes of landscape pattern landscape evolution during the study period. The procedure in response to urbanization,” Landscape and Urban Planning, adopted answered some important question about the vol. 81, no. 4, pp. 341–353, 2007. changes that occurred in the area: Where are the changes? [18] X. J. Yu and C. N. Ng, “Spatial and temporal dynamics of Which are the magnitudes of changes? Which are the urban sprawl along two urban-rural transects: a case study of Guangzhou, China,” Landscape and Urban Planning, vol. 79, directions of changes? Which are the spatial characteristics? no. 1, pp. 96–109, 2007. Further research is needed in order to better understand the [19] D. Geneletti, “Using spatial indicators and value functions to evolution of landcover and landscape in areas in which land assess ecosystem fragmentation caused by linear infrastruc- degradation and desertification processes are occurring. tures,” International Journal of Applied Earth Observation and Geoinformation, vol. 5, no. 1, pp. 1–15, 2004. REFERENCES [20] A. Bianchin and L. Bravin, “Defining and detecting changes in urban areas,” International Archives of Photogrammetry Remote [1] UNCCD (United Nations Convention to Combat Desertifica- Sensing and Spatial Information Sciences, vol. 35, part 7, pp. 466–471, 2004. tion), 1996. United Nations, 2008, http://www.unccd.int/. [2] Desertwatch Project, 2008, http://www.desertnet.org/. [3] Riade Project, 2008, http://www.riade.net/. [4] DesertNet Project, 2008, http://dup.esrin.esa.it/desertwatch/. [5] K. McGarigal, S. A. Cushman, M. C. Neel, and E. Ene, FRAGSTATS: Spatial Pattern Analysis Program for Categor- ical Maps. Computer Software Program, University of Mas- sachusetts, Amherst, Mass, USA, 2002. [6] A. Motroni, S. Canu, G. Bianco, and G. Loj, “Carta delle aree sensibili alla desertificazione (Environmentally sensitive areas to desertification, ESAs),” Servizio Agrometeorologico regionale per la Sardegna, April 2004. [7] C. Kosmas, M. Kirkby, and N. Geeson, Eds., “The Medalus project Mediterranean desertification and land use. Manual on key indicators of desertification and mapping environ- mentally sensitive areas to desertification,” EUR 18882, EC (European Community), Brussels, Belgium, 1999. [8] ERDAS, ERDAS Field Guide, Revised and Expanded, ERDAS, Atlanta, Ga, USA, 5th edition, 2001. [9] ERDAS, ERDAS IMAGINE Tour Guides, Erdas Imagine V8.5, ERDAS, Atlanta, Ga, USA, 2001. [10] Z. Li, X. Li, Y. Wang, A. Ma, and J. Wang, “Land-use change analysis in Yulin prefecture, northwestern China using remote sensing and GIS,” International Journal of Remote Sensing, vol. 25, no. 24, pp. 5691–5703, 2004. [11] D. Lu,P.Mausel, E. Brond´ızio, and E. Moran, “Change detection techniques,” International Journal of Remote Sensing, vol. 25, no. 12, pp. 2365–2407, 2004. [12] W. G. Kepner, C. J. Watts, C. M. Edmonds, J. K. Maingi, S. E. Marsh, and G. 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A Landscape Approach for Detecting and Assessing Changes in an Area Prone to Desertification in Sardinia (Italy)

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Copyright © 2008 Francesca Giordano and Alberto Marini. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Hindawi Publishing Corporation International Journal of Navigation and Observation Volume 2008, Article ID 549630, 5 pages doi:10.1155/2008/549630 Research Article A Landscape Approach for Detecting and Assessing Changes in an Area Prone to Desertification in Sardinia (Italy) 1, 2 1 Francesca Giordano and Alberto Marini TELEGIS Laboratory, Earth Sciences Department, University of Cagliari, Via Trentino 51, 09127 Cagliari, Italy Department State of the Environment and Environmental Metrology, Italian Agency for Environmental Protection and Technical Services (APAT), Via V. Brancati 48, 00144 Rome, Italy Correspondence should be addressed to Francesca Giordano, fragisi@tin.it Received 25 September 2007; Revised 6 December 2007; Accepted 17 March 2008 Recommended by Marco Gianinetto Land degradation and desertification processes represent a serious problem in many areas of Sardinia (Italy), as in the Nurra region where urbanization, overgrazing, and fires have induced environmental degradation and rapid land-use change. In this study, using satellite remote sensing and geographical information system, landcover and landscape change dynamics were investigated. Comparing two Landsat-5 Thematic Mapper, it was possible to assess landcover transformations, and with the FRAGSTATS software it was possible to quantify the changes of landscape characteristics in the Nurra region over a 10-years period. The images were classified into seven landcover types, and a stepwise indicator approach was adopted. The results show a decrease in cropland and an increase of forestland and urban areas. The overall change was estimated to be about 2.5% of the total study area, with two most frequent landcover conversion types: cropland to urban areas and cropland to forestland. Copyright © 2008 F. Giordano and A. Marini. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. INTRODUCTION Due to its particular geographical position and its extreme climatic variations such as droughts and floods, In the last two centuries, the impact of human activities on Sardinia can be considered a representative area of the the land has grown enormously, altering entire landscapes typical environmental problems of the Mediterranean basin. Desertification processes, in particular, represent a serious with important ecological consequences like biodiversity loss, deforestation, soil erosion, and desertification. threat to many areas on the island where the impact of human activities on the environment is overlapped to the According to article 1 of the United Nations Convention climatic stress. In the last decades, the urbanization along to Combat Desertification (UNCCD) desertification is “land the coastal areas has strongly increased due to new tourist degradation in arid, semi-arid and dry sub-humid areas settlements and urban infrastructures. Not only urbanization resulting from various factors, including climatic variations but also the loss of fertile soils, massive water exploitation, and human activities”[1]. Instead, land degradation is a overgrazing, and fires are the other important reasons for reduction in the biological and economic productivity of the environmental problems in the Nurra region, the north- terrestrial ecosystems, including soil, vegetation, and biota. western part of the Island. Many scientific projects such Land degradation also disturbs the ecological, biogeochemi- as DesertNet [2], RIADE [3], and DesertWatch [4]have cal, and hydrological processes. previously investigated this area with results demonstrating Before the presence of the man, natural processes shaped the high sensitivity to desertification of the area. the landscape, but after the man came to Earth, Earth’s face has changed. In the past two centuries, the impact of human The objective of the present study was to detect, char- agricultural, industrial, and extractive activities, combined acterize, and quantify the changes in terms of landcover with natural and human actions, induced climatic variations and landscape in an area sensitive to land degradation and which led to land degradation on an unprecedented scale. desertification in the Nurra region. In the present study, 2 International Journal of Navigation and Observation was used as a reference image to geo-reference the other image within acceptable limits (rms = 0.71 pixels for the 1990 scene), and the nearest neighbour algorithm was used as the resampling method [10]. Atmospheric correction is not strictly necessary when postclassification change detection is the method selected for the analysis of changes. In this case, each image is individually classified and then the resulting maps are compared [11]. In this approach, supervised classification was used in order to perform a landcover map for each image. In this type of classification, the user selects the spectral signatures defined from recognized locations in the image or “training sample.” The computer system then identifies the pixels with similar characteristics and assigns them to a class based on specific criteria. About 50 sample data were defined with help of ortophotos (scale 1 : 10.000) and available maps. These tools helped classify a seven-class legend: urban areas, forestland, shrubland, cropland, barren land, wetlands, and water bodies [12]. Supervised classification was then performed using the maximum likelihood method, in which a pixel with the maximum likelihood is classified Figure 1: Nurra and town of Sassari in Sardinia (Italy). to its corresponding class. The accuracy assessment showed that the overall accuracy of the landcover maps approached 88% for the 1990 image and 92% for the 2000 image. The remote sensing techniques were applied in order to analyze existence of mixed pixels (pixels having more than one class landcover change from 1990 to 2000 by Landsat Thematic in their footprint), in particular among vegetation classes, Mapper TM images interpretation and to assess landscape would require an analysis at a higher geometric resolution structure change by using FRAGSTATS, a spatial pattern or a comparison with multitemporal data to exploit the analysis program software package [5]. phenological curves. This analysis would be useful in order to find if the classification accuracy could further improve. After performing the landcover maps, the next step was to 2. STUDY AREA overlay them and create a landcover change map (1990- The study area (ca 40 43 N, 8 34 E) is located in the 2000). north-western part of Sardinia (Italy) (see Figure 1)and In order to perform the landcover and landscape dynam- covers four municipalities: Sassari, Alghero, Stintino, and ics analysis of the study area, a stepwise indicator approach Porto Torres. This area of about 88400 ha is characterized by was used to measure the various aspects of landcover and a high geological and morphological complexity. The climate landscape and their change [13–15]. is typically Mediterranean dry-subhumid with abundant Landcover maps represent the source of landcover indica- amount of rainfall during the autumn-winter period and a tors (1990 and 2000) and landscape metrics (1990 and 2000). scarce amount of rainfall with very high temperature during In particular, landcover indicators aimed at the evalua- the summer period. Mean annual precipitation values vary tion of the extent of each landcover class in 1990 and in 2000 between 490 mm and 870 mm. The area belongs to the and measured the direction of change (increase/decrease) of phytoclimatic area of Lauretum. each class during the study period. As demonstrated from recent studies [6], the area is Landscape metrics measure the spatial structure of strongly sensitive to land degradation and desertification landcover metrics in terms of composition (number, pro- with more than half of the territory belonging to critical portional frequency, and diversity of landscape elements environmentally sensitive areas (ESAs) or “areas already within the landscape) and configuration (spatial position highly degraded through past misuse, presenting a threat to and distribution of the elements within the landscape). In the environment of the surrounding areas” [7]. order to calculate landscape metrics, landcover maps were converted into Grid format using ERDAS Imagine [8, 9]and introduced into the FRAGSTATS software [5]. 3. METHODS In particular landscape metrics at class level, measuring Two Landsat satellite Thematic Mapper TM images were the aggregate properties of the patches belonging to a single selected over the study area. The first image was a Landsat- class or patch type, and landscape metrics at landscape level, measuring the aggregate properties of the entire patch 5 Thematic Mapper of May 12, 1990, with seven spectral bands and a spatial resolution of 30 m. The second one mosaic, were performed. was a Landsat-5 Thematic Mapper of June 27, 2000. For Landcover change map (1990-2000) represents the source of landcover change indicators, which aim at the the analysis of Landsat satellite images, ERDAS Imagine 8.5 software was used [8, 9]. Landsat 5 TM of June 27, 2000 location of the areas of change and at the evaluation of the F. Giordano and A. Marini 3 Table 1: Landcover areas per classes in the study area, 1990 and 2000. Area in 1990 Area in 2000 Class Class Direction of change ha % ha % Cropland 67.209,5 76,0 Cropland 65.956,9 74,6 decrease Forestland 8.246,4 9,3 Forestland 9.088,6 10,3 increase Shrubland 7.528,8 8,5 Shrubland 7.487,8 8,5 — Urban areas 4.033,2 4,6 Urban areas 4.455,7 5,0 increase Barren 1.064,2 1,2 Barren 1.118,6 1,3 slight increase Wetlands 230,3 0,3 Wetlands 251,9 0,3 slight increase Water bodies 75,9 0,1 Water bodies 28,8 0,0 decrease Total 88.388,2 100,0 Total 88.388,2 100,0 Table 2: Landscape metrics at class level, 1990 and 2000. CA PLAND NP PD AREA MN SHAPE AM PROX MN IJI Class (ha) (%) (#) (#/100 ha) (ha) (ha) (m) (%) Cropland 67210 29,2 1830 0,8 36,8 21,8 56710 63,7 Forestland 8246 3,6 5140 2,2 1,6 5,8 71,2 54,9 Urban areas 4033 1,8 1775 0,8 2,3 5,9 112,2 33,4 Cropland 65957 28,7 1660 0,7 39,7 17,6 66550 69,8 Forestland 9088 3,9 4190 1,8 2,2 7,3 212,2 61,0 Urban areas 4455 1,9 689 0,3 6,5 5,9 301,3 48,0 extent of changes for the total study area and for each increase of riparian vegetation, with a consequent silting up landcover class. These indicators measure also the landcover of the shallow waters, in particular along the coast of the transition direction and the gains and losses for each class Baratz lake. during the study period. The entire set of landcover and landscape indicators 4.2. Landscape indicators at class level has been setup in order to answer the following questions. Where are the landcover changes (location)? Which is the Landcover maps were the source for the landscape metrics magnitude of landcover change (extension)? Which is the computation at class level and at landscape level by means of direction of landcover change (direction)? Which are the FRAGSTATS software [5]. spatial characteristics of landscape change (structure)? Table 2 shows the most relevant landscape metrics performed at class level for the entire study area and for 4. RESULTS the most changing classes (cropland, forestland, and urban areas) [16–18]. For the analysis and the comprehension of 4.1. Landcover indicators landscape metrics at class level, it is advisable not to analyze Once the classification of each image was performed, and the just a landscape metric but rather a set of metrics to better landcover maps for each period of study were obtained, the understand and describe the dynamics of ecosystems and first step was to quantify each landcover class extension over landscape structures. the total area (see Table 1). Cropland in the study area represents the major class of Cropland is largely the dominant landcover type in the the landscape. The study shows a decrease of cropland in study area in both cases, followed by forestland, shrubland, terms of the percentage of landscape (PLAND, proportional and urban areas. In particular during the period from 1990 abundance of each patch type in the landscape), the number to 2000, cropland decreased from 76% to 74.6%, while of patches (NP, extent of subdivision or fragmentation of the forestland and urban areas increased in area (from 9.3% patch type), and the patch density (PD, number of patches to 10.3% and from 4.6% to 5%, resp.) and shrubland on a per unit area) during the period from 1990 to 2000. maintained the same dimensions. Furthermore, a sharp Instead, the area weighted mean shape index (SHAPE AM, decrease of water bodies occurred. mean patch shape complexity weighted by patch area) shows The increase of urban areas, due to the continuous the highest values both in 1990 and 2000 for cropland urbanization process in the Nurra region during the last in the study area and shows a decline during the study years, is not a direct cause of the decrease in the area of water period from 21.8 to 17.6 indicating a reduction in the shape bodies. The variation in water bodies is probably due to the complexity. Shape complexity relates to the geometry of 4 International Journal of Navigation and Observation Table 3: Landscape metrics at landscape level, 1990 and 2000. NP PD LPI AREA MN SHAPE AM PROX MN IJI PR SHDI SHEI Year (#) (#/100 ha) (%) (ha) (ha) (m) (%) — — — 1990 11960 5,2 28,4 7,4 17,9 8750 55,9 7 0,85 0,44 2000 9840 4,3 27,5 9,0 14,6 11390 61,1 7 0,89 0,46 patches whether they tend to be simple and compact or The spatial context of landscape patches also had some irregular and convoluted. changes. For instance, the mean proximity index became In regards to the forestland during the period 1990– greater (from 8750 to 11390) displaying that the spatial dis- 2000, the number of patches (NP) decreased from 5140 tribution of patches became more continuous. In addition, to 4190, the mean patch area (AREA MN, average size of the increase of interspersion and juxtaposition index (from patches) increased from 1.6 to 2.2, and the mean proximity 55.9 to 61.1), together with the increase of mean proximity index (PROX MN, average proximity index for all patches index, indicated a more uniform landscape configuration. in a class) increased from 71.2 to 212.2. This indicates that patches of the same type increasingly occupy the neighbor- 4.4. Landcover change indicators hood between forested patches, which is defined by 300 m, and those patches have become closer and more contiguous From the overlaying of the two landcover maps, a landcover in distribution. In this sense, the forested landscape has change area (1990–2000) was estimated to be about 2.5% of become less fragmented [19]. the total study area. Now, we do not have enough ground The analysis of urban areas indicated that the number of information to be able to rule out errors in the classification. patches (NP) significantly decreased from 1775 to 689, while Considering that there is 8%–12% error in classification the mean patch area (AREA MN) increased from 2.3 to 6.5 accuracy, we expect to improve the analysis through further and the class area (CA, total area of all patches per class) ground points all over the changed areas in order to increase increased from 4033 to 4455. This combination of results the significance of the result. shows that new urban buildings generally occur in the voids Two landcover conversions were the most frequent in the of the core or adjacent to existent urban patches indicating study area: conversion from cropland to urban areas (45.7%) that urban areas grow in a concentrated way [20]. Finally, and conversion from cropland to forestland (44.5%). In the increase of the mean proximity index (from 112 to 301) Sassari, Alghero, and Stintino municipalities, the overlay and of the interspersion and juxtaposition index (from 33,4 of the landcover change map within the administrative to 48,0) of urban class shows a more uniform landscape limits showed that the percentage of landcover was mainly configuration. The first index indicates an increase in the composed by cropland followed by forestland, shrubland, proximity of urban patches, which has become closer and and urban areas. The only difference found was in Porto more contiguous in distribution. As for the second index, it Torres, where the landscape was dominated by cropland measures an increase of the interspersion (or intermixing of followed by urban areas, which represented the second patch types) of urban patches (more equally adjacent to each landscape class in terms of spatial extension. other). The major landcover changes occurred in the Sassari municipality (3%), followed by the Alghero municipality 4.3. Landscape indicators at landscape level (1.7%) and finally by the Porto Torres and Stintino munic- ipalities (1.2%). In particular, the conversion from cropland Table 3 shows the most relevant landscape metrics per- to urban areas represented 52% of the changes in the Sassari formed at landscape level for the study area. area, 26.3% of changes in the Alghero area, and 61.7% of As seen in Table 3, during the decade 1990–2000 the the changes in the Porto Torres area and, in conclusion, patch number (NP) in the study area decreased (from 11960 45.6% of the changes in the Stintino area were related to the to 9840), and the mean patch area (AREA MN) increased conversion from cropland to forestland. (from 7.4 to 9.0), showing a trend towards an increasingly large-grained landscape. Shannon’s diversity (SHDI) and evenness (SHEI) indices 5. CONCLUSIONS both became greater, showing that the landscape heterogene- ity and evenness slightly increased. Shannon’s diversity index The analysis carried out showed that the landscape of the is, in fact, a popular measure of diversity in community study area is dominated by cropland, followed by forestland ecology, applied here to landscapes as a measure of the and shrubland, and urban areas. During the study period equitability of the number of patch types and of the (1990–2000) cropland decreased, while forestland and urban proportional distribution of area among patch types. Shan- areas increased. The dynamics of change revealed that non evenness index is another popular diversity measure forested landscape has become less fragmented during the borrowed from community ecology, indicating the evenness decade thus avoiding the breakup of natural areas into of the distribution of area among the different patch types smaller and more isolated units. As for the urban areas, [5]. they showed growth in a concentrated way, as new urban F. Giordano and A. Marini 5 buildings generally occurred in the voids of the core or evaluating change in a semi-arid environment,” Environmental Monitoring and Assessment, vol. 64, no. 1, pp. 179–195, 2000. adjacent to existent urban patches. [13] F. Herzog and A. Lausch, “Supplementing land-use statistics The overall landcover change in the area was estimated to with landscape metrics: some methodological considerations,” be about 2.5% of the total study area, with two most frequent Environmental Monitoring and Assessment, vol. 72, no. 1, pp. landcover conversion types: 37–50, 2001. (i) conversion from cropland to urban areas (45.7%), [14] A. Lausch, “Assessment of landscape pattern and landscape functions by application of GIS and remote sensing,” in (ii) conversion from cropland to forestland (44.5%). Proceedings of the 3rd International Conference on Ecosystems The major landcover changes occurred in the Sassari munic- and Sustainable Development (ECOSUD ’01),Y.Villacampa, ipality (3%), followed by the Alghero municipality (1.7%). C. A. Brebbia, and J. L. Uso, Eds., vol. 10, pp. 367–376, The present work describes the analysis performed in WITPRESS, Alicante, Spain, June 2001. [15] A. Lausch and H. Thulke, “The analysis of spatio-temporal terms of landcover and landscape change by means of dynamics of landscape structures,” in Landscape Balance and remote sensing and GIS techniques in an area prone to land Landscape Assessment, R. Kronert, U. Steinhardt, and M. Volk, degradation and desertification in Sardinia (Italy) during the Eds., pp. 113–136, Springer, Berlin, Germany, 2001. period 1990–2000. For this purpose, a set of landcover and [16] S. Weiers, M. Bock, M. Wissen, and G. 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Which are the urban sprawl along two urban-rural transects: a case study of Guangzhou, China,” Landscape and Urban Planning, vol. 79, directions of changes? Which are the spatial characteristics? no. 1, pp. 96–109, 2007. Further research is needed in order to better understand the [19] D. Geneletti, “Using spatial indicators and value functions to evolution of landcover and landscape in areas in which land assess ecosystem fragmentation caused by linear infrastruc- degradation and desertification processes are occurring. tures,” International Journal of Applied Earth Observation and Geoinformation, vol. 5, no. 1, pp. 1–15, 2004. REFERENCES [20] A. Bianchin and L. Bravin, “Defining and detecting changes in urban areas,” International Archives of Photogrammetry Remote [1] UNCCD (United Nations Convention to Combat Desertifica- Sensing and Spatial Information Sciences, vol. 35, part 7, pp. 466–471, 2004. tion), 1996. United Nations, 2008, http://www.unccd.int/. 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